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Mobile Virtual Network Operators
Mobile Virtual Network Operators
Sasken’s suite of digital IT capabilities is complemented by over 25 years of embedded and devices expertise, making it an ideal innovation partner for the world’s leading enterprises. Sasken is focused on Physical to Digital (P2D) transformation that enriches enterprises’ competitive advantage and helps them achieve business objectives.
Mobile Virtual Network Operators (MVNOs) are faced with myriad challenges such as integrating revenue, CDR and international calling data to provide comprehensive insights to business teams. For them, need based customer segmentation is much more effective than transaction based segmentation. Also, knowing the likelihood of customer attrition ahead of time could be a big competitive advantage for an MVNO.
Sasken has helped one of the world’s largest MVNOs to optimize their wholesale bundle costs helping them save millions of Euros in carrier costs. Harnessing the latent power of data, Sasken’s predictive and prescriptive analytics solution leverages statistical models to predict customer demand and determine appropriate supply-side decisions. Sasken is implementing an actionable insights platform leveraging the latest data technologies to enable business users to visualize and uncover insights.
Sasken provides MVNO BI insights big platform based on standard technologies such as Apache Hive, AWS/Redshift/Tableau to provide fast data reloads, ad-hoc granular queries and self-service BI platform. Sasken has deep domain understanding of MVNOs and understands how to address key business problems such as fraud detection, incomplete billing, matching invoicing with revenue and other marketing/financial KPIs using the solution.
Sasken is helping one of the largest MVNOs in the world with predictive churn modelling of their high value customers. The modelling can be used to trigger specific marketing measures to reduce churn. Sasken’s solution leverages daily usage information and customer support calls to predict possibility of customer churning, and based on propensity to churn and Average Revenue Per User, identify customers who should be targeted for specific retention measures.
Sasken is assisting one of the leading MVNOs in the world to implement need-based segmentation, to help in more targeted, personalized, and effective marketing pitches. Sasken solution involves identifying a sample of customers to administer a survey to understand their key communication needs. The sampled customers are then grouped into various segments. Using sophisticated statistical techniques all the customers are mapped into the sampled customer segments using the customer’s transaction data. This serves as a very effective need-based segmentation to enable MVNOs to provide targeted and personalized pitches.
Sasken uses analytical models to optimize customer’s marketing spend allocation, improve marketing execution and help simulate top-line impact of various what-if scenarios. We use historical sales data, marketing spend data by inputs and use statistical modelling to determine drivers for various marketing inputs. This could then be used to simulate the marketing objectives and optimize marketing inputs within budget constraints.
Sasken has built an innovative call graph big data analytics solution that analyses CDR data to build graph, identify communities and to find influencers. We are using this solution with a leading MVNO to help with their campaigns and offers.
Sasken has helped one of the largest MVNOs in the world to optimize their wholesale bundle costs using prescriptive analytical models for usage resulting significant impact to their bottom line. The solution delivered more than 10X returns on investment.. Sasken has a lightweight engagement model so that a 6-8 weeks pilot could be performed to prove the solution before a bigger capex commitment.
Scope Project related data stored in discrete systems, thereby bringing in inefficiencies in tracking Solution Created a flexible and robust Auto-ID and sensor analytics platform for... more